A full-scale treatment method for landfill leachate

Abstract

The application relates to the technical field of wastewater treatment control, and discloses a full-quantitative treatment method for landfill leachate, which comprises the following steps: a sensor baseline is established by adopting steady-state window statistics, and a known pulse is applied on site to obtain a calibration coefficient; a robust deviation index is calculated in real time, and a composite abnormal index is formed according to the maximum value rule to determine a transient front; the chemical type and the particle type are distinguished based on the ratio of representative sensors; a split ratio is determined according to a segmentation rule, and the dosage of a medicament and a membrane protection action are calculated according to an algebraic relationship in combination with the on-site calibration coefficient; the effect is evaluated in a short-term response window, and the baseline is updated or recalibration is triggered.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment and control technology, specifically a method for the full-scale treatment of landfill leachate. Background Technology

[0002] Landfill leachate is typically treated in a fully continuous manner in landfills, incinerators, and centralized treatment facilities. Its influent quality is highly volatile and prone to sudden changes due to rainfall, changes in waste composition, and operating conditions. In the long term, in addition to regular load changes, there may be uncommon conditions such as a rapid increase in dissolved organic matter or the concentrated entry of colloids or fine particles into the system. If these conditions are not identified and handled properly in a timely manner, they can easily lead to biochemical system inhibition, increased membrane fouling, or instability of the treatment unit. Existing leachate treatment systems mostly rely on fixed thresholds, empirical criteria, or single water quality indicators for monitoring and control, making it difficult to accurately characterize the degree of abnormal deviation under conditions where the water quality baseline changes over time. Although some systems introduce multi-parameter joint judgment, the differentiation of abnormal types is still dominated by human experience or single indicators, making it difficult to effectively distinguish between chemical and particulate aberrations, thus affecting the rationality of subsequent diversion, chemical dosing, and protection strategies. In terms of chemical dosing and membrane protection control, existing technologies usually calculate based on design parameters or static empirical values, lacking calibration standards that match the real-time water quality characteristics on site, causing the dosage and actions to deviate from actual needs under complex operating conditions. In the continuous and high-risk application scenario of full-scale leachate treatment, the above problems are particularly prominent when facing uncommon but highly destructive transient water quality changes. Therefore, there is an urgent need for a technical solution that can form a stable baseline understanding based on online multi-source monitoring data under continuous operation conditions, and uniformly determine, classify, and link short-term anomalies with processes, so as to adapt to the complex, variable, and unpredictable actual operating environment in the process of landfill leachate treatment. Summary of the Invention

[0003] This invention provides a method for the full-scale treatment of landfill leachate, which helps to solve the problems mentioned in the background art.

[0004] This invention provides the following technical solution: a method for the full-volume treatment of landfill leachate, comprising: Before the system is put into operation, raw time-series data within the steady-state window are collected, and baseline statistics for each sensor are established. Under steady-state conditions, a known pulse is applied and the response is recorded to obtain and store the field calibration coefficients. Real-time reading of sensor measurements and calculation of robust deviation index based on baseline statistics; The deviation indicators of each sensor are summarized according to the maximum value rule to form a composite anomaly indicator, which is then compared with a preset threshold to determine the occurrence of an anomaly. Anomaly types are identified based on the deviation ratio of representative sensors and threshold rules; Based on the identified anomaly type, determine the processing mode and traffic diversion strategy, and calculate the diversion ratio; Based on the split ratio and the on-site calibration coefficient, the required dosage of reagent and membrane protection action parameters are calculated using algebraic relationships, and execution instructions are issued. Monitor and respond within the short-term response window, and update the baseline or calibration or trigger recalibration according to the rules to keep the parameters synchronized with the baseline.

[0005] Optionally, the step of collecting raw time-series data within the steady-state window before the system is put into operation and establishing baseline statistics for each sensor includes: Set the steady-state observation window to 60 to 120 minutes, and record the measured values ​​of each sensor within this window at a preset sampling frequency; For each sensor, calculate and record its median value within this window; For each sensor, calculate and record its absolute median difference within this window; The median and the absolute median difference are used as baseline statistics for subsequent anomaly detection and calibration and stored in the controller.

[0006] Optionally, the step of applying a known pulse under steady-state conditions and recording the response to obtain and store field calibration coefficients includes: Under steady-state conditions, administer a known dose of pulsatile drug and record the average turbidity within the first ten minutes before administration and the average steady-state turbidity within the ten to thirty minute window after administration. If the turbidity does not decrease after administration, adjust the dosage and repeat until a decrease in turbidity is observed. The turbidity reduction coefficient per unit dose of flocculant is determined by the ratio of the change in turbidity before and after drug administration to the known dosage. Simultaneously, the changes in the light scattering ratio before and after the pulse were recorded, and a linear calibration coefficient between scattering and turbidity was established accordingly. Under safe and controlled conditions, a preset dose of oxidant is applied, and the average value of the dissolved organic matter indication before and after the oxidant is applied is recorded. The oxidant metering coefficient is established based on the normalized relationship between the application response and the application flow rate. The actual dissolution time of the agent in the reaction tank after dosing was measured and compared with the estimated ideal dissolution time. The mixing efficiency factor was determined by the ratio of the two and recorded.

[0007] Optionally, the real-time reading of sensor measurements and calculation of robustness bias index based on baseline statistics includes: For each sensor, its current measured value is converted with the baseline median and the absolute median difference of the baseline. A conversion factor is used to convert the absolute median difference into a scale comparable to the standard deviation. The robustness deviation index of the sensor is then calculated and used for subsequent multi-parameter anomaly determination.

[0008] Optionally, the step of summarizing the deviation indices of each sensor according to the maximum value rule to form a composite anomaly index, and comparing it with a preset threshold to determine the occurrence of an anomaly, includes: The absolute value of the robust deviation index of each sensor in the preset sensor set used for anomaly detection is taken, and the maximum value rule is used to summarize it into a multidimensional anomaly index. The threshold for multidimensional anomaly detection is set to a scale range of four to six. When the multidimensional anomaly index reaches or exceeds the preset threshold, a transient anomaly is determined to exist.

[0009] Optionally, the method of determining the anomaly type based on the deviation ratio of representative sensors and threshold rules includes: When an anomaly is detected, robust deviation indices for dissolved organic matter, turbidity, and redox potential are selected. The ratio of dissolved organic matter deviation index to turbidity deviation index was used as a chemical index; a particle size proxy was constructed based on the linear calibration relationship between light scattering and turbidity. When the chemical index is not less than the preset ratio and the redox potential deviation reaches the preset negative threshold, the abnormality is judged to be chemical type. When the chemical properties index is less than the preset ratio and the particle size distribution exceeds the absolute median difference of its baseline median plus one and a half times, the abnormality is determined to be particulate.

[0010] Optionally, the step of determining the processing mode and traffic splitting strategy based on the determined anomaly type, and calculating the traffic splitting ratio, includes: When a chemical anomaly is detected, the diverted water is transported to an isolation tank via a bypass, and an oxidant is introduced into the isolation line for dilution and oxidation. When a particulate anomaly is identified, flocculant is added to the main line to enhance flocculation, and the membrane load ratio in the membrane treatment section is temporarily reduced. Based on the magnitude of the chemical properties, the volume fraction of the flow diverted to the isolation tank is determined using a segmented rule: Full diversion is implemented when chemical indicators reach or exceed strict thresholds; Partial diversion is implemented when the chemical properties are between two thresholds; When the chemical index is below the threshold, diversion to the isolation tank will not be implemented.

[0011] Optionally, the step of calculating the required reagent dosage and membrane protection action parameters based on the split ratio and on-site calibration coefficient using an algebraic relationship, and issuing execution instructions, includes: After determining the integral rate of the fluid, the target turbidity of the main line is set as the absolute median difference of the median turbidity within the steady-state observation window plus a multiple of 1.5. Based on the actual volumetric flow rate and current turbidity of the main line after diversion, combined with the flocculant efficiency coefficient and mixing efficiency factor obtained from on-site calibration, the required total flocculant dosage is determined by algebraic calculation and a dosing instruction is issued. Based on the flow rate diverted to the isolation line and the difference between the current dissolved organic matter value and its baseline median, the required oxidant dosage is determined through algebraic calculation using the oxidant metering coefficient, and a dosing instruction is issued. A combined threshold criterion of short-term membrane pressure difference increase and particle size proxy is used to trigger membrane backwashing operation.

[0012] Optionally, the step of monitoring and handling the response within a short response window, and updating the baseline or calibration or triggering recalibration according to rules to keep the parameters synchronized with the baseline includes: After the treatment is performed, the turbidity changes before and after the treatment are compared with the expected changes calculated based on the dosage and calibration coefficient within a preset short time window to determine whether the treatment has achieved the predetermined response. When the actual response does not meet the expected value, a controlled short pulse recalibration is performed to update the field calibration coefficients; According to the preset update interval, the steady-state observation window is periodically resampled, and the median and absolute median difference of each sensor are recalculated to update the baseline statistics.

[0013] The present invention has the following beneficial effects: 1. This technical solution is designed for the full-scale, continuous operation of landfill leachate treatment environments, focusing on occasional but high-risk water quality mutations during operation. In such environments, the influent water quality not only fluctuates significantly over the long term but may also experience unusual conditions such as a sudden increase in dissolved organic matter or the concentrated entry of fine particles or colloids in a short period. If fixed thresholds or empirical methods are still used for judgment, it is easy to cause delays or improper handling. Therefore, this solution establishes a water quality baseline under stable operating conditions and identifies the degree of deviation in real time based on this baseline, thereby distinguishing different types of abnormal changes and linking treatment actions such as diversion, chemical dosing, and membrane protection. This enables the treatment system to have the ability to perceive and respond to sudden changes in water quality in real time, rather than relying on post-event corrections. In the specific application scenario of landfill leachate with high load and high uncertainty, this solution can effectively reduce the impact risk of abnormal water quality on the biological unit and membrane system without affecting the continuity of full-scale treatment, making the treatment behavior more in line with the actual on-site conditions, thereby improving the stability and controllability of system operation. 2. By continuously collecting multiple online water quality parameters and establishing an operational baseline during the stable operation phase of the landfill leachate treatment system, the system's understanding of its "normal operating state" is based on actual working conditions rather than design assumptions. This approach provides a clear reference basis for judging subsequent water quality changes, thus avoiding the problem of thresholds gradually becoming invalid due to long-term slow fluctuations in leachate. In a fully continuous treatment environment, this baseline mechanism can effectively distinguish between the system's own slow drift and sudden water quality changes, thereby providing a stable and reliable basis for subsequent anomaly identification, reducing the probability of misjudgment and missed judgment during long-term operation, and improving the system's adaptability to complex operating environments. 3. By introducing controlled pulses under stable operating conditions and establishing a corresponding relationship with the on-site water quality response, the key relationship on which subsequent treatment actions are based is directly derived from the actual response characteristics of the system. This approach avoids the deviation caused by judging solely based on theoretical parameters or design values. In scenarios where the composition of leachate water is complex and varies greatly, it can make subsequent control actions closer to the actual treatment capacity boundary, thereby reducing the occurrence of insufficient or excessive treatment under abnormal operating conditions and enhancing the controllability and reliability of the system when sudden load changes occur. 4. By comparing the real-time collected water quality data with the established operating baseline and calculating the degree of deviation, the anomaly identification is based on the magnitude of change rather than the absolute value. This method can still accurately capture the occurrence of abnormal changes when the overall leachate water quality level changes with the season or landfill stage, avoiding the masking of anomalies due to the overall increase in water quality, and avoiding frequent false triggers due to the overall decrease in water quality, so that the system can maintain stable identification sensitivity in long-term operation. 5. By summarizing multiple water quality deviation information and forming a comprehensive judgment result, the system disturbance caused by triggering treatment actions due to the abnormality of a single indicator is avoided. This comprehensive approach makes the system pay more attention to the degree of abnormality of the overall operating status, and can respond quickly when multiple parameters change simultaneously in a short period of time, while maintaining stable operation when a single parameter fluctuates occasionally. This reduces the frequency of ineffective actions of the system under complex water intake conditions and improves the continuity of the treatment process. 6. By further distinguishing the nature of the abnormal changes after the anomaly is identified, the system can identify whether the anomaly is mainly due to changes in solubility load or changes in particulate matter. This differentiation mechanism makes the subsequent treatment strategy more targeted and avoids mismatch problems caused by using the same treatment path for anomalies of different natures. When high-risk chemical mutations or particulate shocks occur in landfill leachate, it can provide the system with a clear direction for response and improve the effectiveness of anomaly handling. 7. By directly linking the anomaly type judgment result to the treatment mode selection, the decision-making of diversion, isolation or enhanced treatment no longer depends on human intervention. Under full-scale treatment conditions, this linkage mechanism can separate or slow-release high-risk water bodies in the early stage of anomaly occurrence, reducing their impact on the main treatment unit. In environments where leachate treatment systems operate continuously and downtime costs are high, it significantly improves the system's buffering capacity against sudden risks. 8. By calculating the intensity of chemical dosing and treatment actions based on site conditions, the treatment behavior is made to correspond with the current water quality status. This method avoids overtreatment when the abnormality is small and undertreatment when the abnormality is large. In application scenarios where leachate water quality changes drastically and irregularly, the treatment intensity can be changed with the degree of abnormality, thereby improving resource utilization efficiency and maintaining stable system operation. 9. By conducting short-term observations of the handling response after an anomaly is handled and adjusting the system's understanding accordingly, the system can continuously reflect the true operating status. In environments where the composition of leachate water changes significantly in stages, this approach can avoid the long-term use of outdated operating knowledge, ensuring that the system maintains a consistent understanding of the current operating conditions. This reduces subsequent anomaly judgment biases and enhances the system's adaptability to complex operating stages. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the basic process of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1, refer to Figure 1 A method for the full-volume treatment of landfill leachate, comprising: Before the system is put into operation, raw time-series data within the steady-state window are collected, and baseline statistics for each sensor are established. Under steady-state conditions, a known pulse is applied and the response is recorded to obtain and store the field calibration coefficients. Real-time reading of sensor measurements and calculation of robust deviation index based on baseline statistics; The deviation indicators of each sensor are summarized according to the maximum value rule to form a composite anomaly indicator, which is then compared with a preset threshold to determine the occurrence of an anomaly. Anomaly types are identified based on the deviation ratio of representative sensors and threshold rules; Based on the identified anomaly type, determine the processing mode and traffic diversion strategy, and calculate the diversion ratio; Based on the split ratio and the on-site calibration coefficient, the required dosage of reagent and membrane protection action parameters are calculated using algebraic relationships, and execution instructions are issued. Monitor the response within a short response window and update the baseline, calibration, or trigger recalibration according to rules to maintain parameter synchronization with the baseline. Specifically, the membrane is a membrane device used for solid-liquid separation in leachate treatment engineering; the membrane backwashing is a short-term reverse or high-speed flushing of the membrane to remove adhering contaminants. The process of collecting raw time-series data within a steady-state window before the system is put into operation and establishing baseline statistics for each sensor includes: Set the steady-state observation window to 60 to 120 minutes, and record the measured values ​​of each sensor within this window at a preset sampling frequency; For each sensor, calculate and record its median value within this window; For each sensor, calculate and record its absolute median difference within this window; The median and the absolute median difference are used as baseline statistics for subsequent anomaly detection and calibration and stored in the controller.

[0017] The process of applying a known pulse under steady-state conditions and recording the response to obtain and store field calibration coefficients includes: Under steady-state conditions, administer a known dose of pulsatile drug and record the average turbidity within the first ten minutes before administration and the average steady-state turbidity within the ten to thirty minute window after administration. If the turbidity does not decrease after administration, adjust the dosage and repeat until a decrease in turbidity is observed. The turbidity reduction coefficient per unit dose of flocculant is determined by the ratio of the change in turbidity before and after drug administration to the known dosage. Simultaneously, the changes in the light scattering ratio before and after the pulse were recorded, and a linear calibration coefficient between scattering and turbidity was established accordingly. Under safe and controlled conditions, a preset dose of oxidant is applied, and the average value of the dissolved organic matter indication before and after the oxidant is applied is recorded. The oxidant metering coefficient is established based on the normalized relationship between the application response and the application flow rate. The actual dissolution time of the agent in the reaction tank after dosing was measured and compared with the estimated ideal dissolution time. The mixing efficiency factor was determined by the ratio of the two and recorded.

[0018] The real-time reading of sensor measurements and the calculation of robust deviation indices based on baseline statistics include: For each sensor, its current measured value is converted with the baseline median and the absolute median difference of the baseline. A conversion factor is used to convert the absolute median difference into a scale comparable to the standard deviation. The robustness deviation index of the sensor is then calculated and used for subsequent multi-parameter anomaly determination.

[0019] The step of summarizing the deviation indices of each sensor according to the maximum value rule to form a composite anomaly index, and comparing it with a preset threshold to determine the occurrence of an anomaly, includes: The absolute value of the robust deviation index of each sensor in the preset sensor set used for anomaly detection is taken, and the maximum value rule is used to summarize it into a multidimensional anomaly index. The threshold for multidimensional anomaly detection is set to a scale range of four to six. When the multidimensional anomaly index reaches or exceeds the preset threshold, a transient anomaly is determined to exist.

[0020] The method for identifying anomaly types based on the deviation ratio and threshold rules of representative sensors includes: When an anomaly is detected, robust deviation indices for dissolved organic matter, turbidity, and redox potential are selected. The ratio of dissolved organic matter deviation index to turbidity deviation index was used as a chemical index; a particle size proxy was constructed based on the linear calibration relationship between light scattering and turbidity. When the chemical index is not less than the preset ratio and the redox potential deviation reaches the preset negative threshold, the abnormality is judged to be chemical type. When the chemical properties index is less than the preset ratio and the particle size distribution exceeds the absolute median difference of its baseline median plus one and a half times, the abnormality is determined to be particulate.

[0021] The process of determining the processing mode and traffic splitting strategy based on the identified anomaly type, and calculating the traffic splitting ratio, includes: When a chemical anomaly is detected, the diverted water is transported to an isolation tank via a bypass, and an oxidant is introduced into the isolation line for dilution and oxidation. When a particulate anomaly is identified, flocculant is added to the main line to enhance flocculation, and the membrane load ratio in the membrane treatment section is temporarily reduced. Based on the magnitude of the chemical properties, the volume fraction of the flow diverted to the isolation tank is determined using a segmented rule: Full diversion is implemented when chemical indicators reach or exceed strict thresholds; Partial diversion is implemented when the chemical properties are between two thresholds; When the chemical index is below the threshold, diversion to the isolation tank will not be implemented.

[0022] The process involves calculating the required reagent dosage and membrane protection action parameters based on the split ratio and on-site calibration coefficient using algebraic relationships, and issuing execution instructions, including: After determining the integral rate of the fluid, the target turbidity of the main line is set as the absolute median difference of the median turbidity within the steady-state observation window plus a multiple of 1.5. Based on the actual volumetric flow rate and current turbidity of the main line after diversion, combined with the flocculant efficiency coefficient and mixing efficiency factor obtained from on-site calibration, the required total flocculant dosage is determined by algebraic calculation and a dosing instruction is issued. Based on the flow rate diverted to the isolation line and the difference between the current dissolved organic matter value and its baseline median, the required oxidant dosage is determined through algebraic calculation using the oxidant metering coefficient, and a dosing instruction is issued. A combined threshold criterion of short-term membrane pressure difference increase and particle size proxy is used to trigger membrane backwashing operation.

[0023] The monitoring and handling of responses within a short response window, and updating the baseline or calibration or triggering recalibration according to rules to maintain parameter synchronization with the baseline, includes: After the treatment is performed, the turbidity changes before and after the treatment are compared with the expected changes calculated based on the dosage and calibration coefficient within a preset short time window to determine whether the treatment has achieved the predetermined response. When the actual response does not meet the expected value, a controlled short pulse recalibration is performed to update the field calibration coefficients; According to the preset update interval, the steady-state observation window is periodically resampled, and the median and absolute median difference of each sensor are recalculated to update the baseline statistics. This technical solution is designed for the full-scale, continuous treatment of landfill leachate, specifically addressing occasional but high-risk water quality anomalies during operation. In such environments, influent water quality not only fluctuates significantly over the long term but may also experience unusual conditions such as a sudden increase in dissolved organic matter or the concentrated entry of fine particles or colloids within a short period. Relying on fixed thresholds or empirical methods for judgment can easily lead to delayed or inappropriate treatment. Therefore, this solution establishes a water quality baseline under stable operating conditions and identifies deviations in real time, distinguishing different types of abnormal changes and triggering corresponding treatment actions such as diversion, chemical dosing, and membrane protection. This enables the treatment system to have immediate perception and targeted response capabilities when water quality changes suddenly, rather than relying on post-event corrections. In the specific application scenario of landfill leachate, characterized by high load and high uncertainty, this solution effectively reduces the impact risk of abnormal water quality on the biological treatment unit and membrane system without affecting the continuity of full-scale treatment, making the treatment behavior more closely aligned with actual on-site conditions, thereby improving the stability and controllability of system operation. Example 2, a method for full-volume treatment of landfill leachate, further includes: The process of collecting raw time-series data within a steady-state window before the system is put into operation and establishing baseline statistics for each sensor includes: Setting the steady-state observation window and in Internal sampling frequency set Record the measurements from each sensor. ,in Pick The sampling frequency Take as Smaller values ​​result in more frequent sampling, which can better capture fast and short-lived transient fronts, improve the temporal resolution of anomaly identification, and reduce the probability of missing short peaks. They can more accurately estimate the instantaneous slope when pulse calibration or when the membrane TMP changes rapidly, but this increases the amount of data, noise, and may increase the probability of false triggering. Larger values ​​result in sparser sampling, saving data bandwidth, reducing instantaneous noise interference, and providing more stable long-term statistics. However, they are more prone to missing short-lived anomalies, are less sensitive to the TMP growth rate criterion for backwash triggering, and reduce the accuracy of instantaneous response estimation in pulse calibration. Calculate and record the median and MAD for each sensor: ; ; in: :sensor At any moment Real-time measurement values; :sensor In the window the median; :sensor The absolute median difference. By continuously collecting multiple online water quality parameters and establishing an operational baseline during the stable operation phase of the landfill leachate treatment system, the system's understanding of "normal operating conditions" is based on actual working conditions rather than design assumptions. This approach provides a clear reference basis for judging subsequent water quality changes, thus avoiding the problem of thresholds gradually becoming invalid due to long-term slow fluctuations in leachate. In a fully continuous treatment environment, this baseline mechanism can effectively distinguish between the system's own slow drift and sudden water quality changes, thereby providing a stable and reliable basis for subsequent anomaly identification, reducing the probability of misjudgment and missed judgment in long-term operation, and improving the system's adaptability to complex operating environments. The process of applying a known pulse under steady-state conditions and recording the response to obtain and store field calibration coefficients includes: Set the pulse dosage as The specific values ​​are specified based on volume as the impact on the reaction vessel. The drug dosage for the expected change in turbidity, The reaction tank is set up with g—100g, this value should be adjusted according to the drug activity and safety limits; Record the average turbidity within 10 minutes before drug administration. After administering the medication, at the window Inner steady-state average The window Pick ; Calculate turbidity change ;like If the test is deemed ineffective, adjust the dosage and try again until... ; Calculate the efficiency coefficient: ; in: : Turbidity reduction coefficient per unit drug dose; The known mass of the agent administered via pulse; Changes in turbidity before and after the pulse; Simultaneously, light scattering changes were measured and recorded after pulsed drug administration. With the corresponding And calculate: ; in: Change in light scattering ratio before and after pulsed drug administration; Scattering-turbidity ratio coefficient; Deploy the pre-set plan under safe and controlled conditions. Dosage of oxidizing agent, and selection of a soluble organic compound. Observe its response, specifically including: Record Before and after the addition of oxidant Average measurement within and And based on this, calculate the post-dosing Change: The window Pick ; Statistical oxidant stoichiometric coefficient: ; in: : The flow base used for normalization, set as the instantaneous volumetric flow rate actually affected by the added oxidant during this oxidation pulse; Dissolution experiments were conducted on the added reagents in the reaction tank, including: The actual time required for the reagent to dissolve after being added to the reaction tank is measured. ; Obtain the ideal dissolution time of the drug as estimated based on its properties. ; calculate ; in: Mixed efficiency factor. By introducing controlled pulses under stable operating conditions and establishing a corresponding relationship with the on-site water quality response, the key relationship upon which subsequent treatment actions are based is directly derived from the actual response characteristics of the system. This approach avoids the bias caused by judging solely based on theoretical parameters or design values. In scenarios where the composition of leachate water is complex and varies greatly, it enables subsequent control actions to be closer to the actual treatment capacity boundary, thereby reducing the occurrence of undertreatment or overtreatment under abnormal operating conditions and enhancing the controllability and reliability of the system during sudden load changes. The real-time reading of sensor measurements and the calculation of robust deviation indices based on baseline statistics include: Calculate the robust z-score using the current sensor measurement relative to its baseline median and MAD: ; in: :sensor The current measurement value; :sensor Baseline statistics; constants : Convert MAD to a scale comparable to the standard deviation, which is a numerical constant; :sensor Robust deviation index. By comparing real-time collected water quality data with the established operating baseline and calculating the degree of deviation, anomaly identification is based on the magnitude of change rather than the absolute value. This method can accurately capture the occurrence of abnormal changes even when the overall leachate water quality level changes with the season or landfill stage. It avoids anomalies being masked by an increase in overall water quality and frequent false triggers caused by a decrease in overall water quality, thus enabling the system to maintain stable identification sensitivity during long-term operation. The step of summarizing the deviation indices of each sensor according to the maximum value rule to form a composite anomaly index, and comparing it with a preset threshold to determine the occurrence of an anomaly, includes: The L∞ norm of each sensor's z-score is used as a multidimensional anomaly indicator: ; in: A pre-defined set of sensors for anomaly detection; :time The composite abnormal indicators; Let the anomaly detection threshold be... Values ; If it exists This indicates a transient front, suggesting an anomaly. The determination threshold Take the initial This refers to the 5σ level under the MAD scale, and an adjustment mechanism is used to assess whether to adjust it within 24–72 hours after startup, based on the statistics of false trigger rate and unprocessed events; if taken A smaller value increases sensitivity but may lead to more short-term false triggers, making the system more conservative and requiring more diversion / protection actions; if... Larger values ​​reduce false triggering but may miss weak fronts. By summarizing multiple water quality deviations and forming a comprehensive judgment result, the system avoids system disturbances caused by triggering treatment actions due to a single abnormal indicator. This comprehensive approach allows the system to pay more attention to the degree of abnormality in the overall operating state, enabling it to respond quickly when multiple parameters change simultaneously in a short period of time, and maintain stable operation when a single parameter fluctuates occasionally. This reduces the frequency of ineffective actions of the system under complex influent conditions and improves the continuity of the treatment process. The method for identifying anomaly types based on the deviation ratio and threshold rules of representative sensors includes: When an anomaly is detected, the dissolved organic matter is selected for observation. turbidity and redox potential The specific steps to distinguish between abnormal "chemical type" and "particulate type" are as follows: Get , and Corresponding robustness deviation index , and ; calculate: ; ; like and The anomaly was determined to be a chemical organic mutation; if and If so, the anomaly is determined to be granular; in: To prevent small positive numbers with a denominator of zero, the value should be [value missing]. ; : Chemical index ratio; Particle size proxy; Scattering-turbidity ratio coefficient; The current turbidity value collected; The ratio threshold is 2-3. A larger value will more strictly identify it as particulate and reduce the chemical bypass. If the UV change significantly exceeds the turbidity change, i.e. the ratio is large, it is more likely to be a change in the solubility chemical load. : ORP's anomaly threshold, value A value of 4 is acceptable, meaning that an abnormality is considered when the ORP decrease is significant enough to be equivalent to approximately 4 MAD levels. Smaller values ​​are more sensitive, allowing for earlier detection of slight ORP decreases and earlier identification of potential organic toxic inputs, thus triggering bypass or oxidation measures more quickly. However, if the ORP sensor itself has high noise or ORP fluctuates frequently, it may lead to more false alarms, mistaking non-dangerous short-term fluctuations for chemical toxicity events, resulting in frequent and costly bypasses or unnecessary dosing. Larger values ​​are more conservative, reducing false alarms and unnecessary bypasses or dosing, but may miss weak but still harmful chemical inputs, causing damage to the biochemical unit or downstream treatment. , : respectively within the steady-state observation window Internal statistics The median and MAD. By further distinguishing the nature of the abnormal changes after the anomaly is identified, the system can identify whether the anomaly is mainly due to changes in dissolved load or particulate matter. This differentiation mechanism makes subsequent treatment strategies more targeted, avoiding mismatch problems caused by using the same treatment path for anomalies of different natures. When high-risk chemical mutations or particulate shocks occur in landfill leachate, it can provide the system with a clear direction for response and improve the effectiveness of anomaly handling. The process of determining the processing mode and traffic splitting strategy based on the identified anomaly type, and calculating the traffic splitting ratio, includes: When the anomaly is determined to be chemical, it is diverted to an isolation tank via a bypass and oxidant is added for dilution and oxidation. When the anomaly is determined to be particulate, flocculant is added to enhance flocculation and the load ratio in the membrane section is temporarily reduced. Specifically, the temporary reduction of the load ratio in the membrane section means temporarily reducing the permeate flow rate per unit membrane area or unit or reducing its treatment load intensity in the membrane treatment unit to reduce the risk of a sudden drop in membrane flux caused by transient high load or particle enrichment. This can be achieved by reducing the permeate pump speed or lowering the filter membrane suction load to reduce the flux per square meter of membrane. Based on the ratio of chemical properties Define the volume fraction of the diversion to the isolation tank: ; in: : Volume fraction of the diverted fluid to the isolation tank; Ratio threshold; A strict ratio threshold of 4-5 is used, meaning that in cases of extremely strong chemical anomalies, all water bodies are isolated. Higher values ​​result in less frequent full isolation, while lower values ​​indicate a more conservative approach. By directly linking the anomaly type assessment to the treatment mode selection, decisions regarding diversion, isolation, or enhanced treatment no longer rely on human intervention. Under full-scale treatment conditions, this linkage mechanism can separate or release high-risk water bodies at the initial stage of anomalies, reducing their impact on the main treatment unit. In environments where leachate treatment systems operate continuously and downtime costs are high, this significantly enhances the system's buffering capacity against sudden risks. The process involves calculating the required reagent dosage and membrane protection action parameters based on the split ratio and on-site calibration coefficient using algebraic relationships, and issuing execution instructions, including: After determining the flow rate, the flocculant dosage, oxidant dosage, and membrane backwash trigger conditions are calculated as follows: Set the expected safe value for the mainline turbidity after diversion to... : Pick ;in, and For steady-state observation window Median turbidity and MAD within; Calculate the current mainline volumetric flow rate ,in The volume fraction of the flow diverted to the isolation tank; The measured instantaneous flow rate of the total system influent; Get current turbidity And calculate the required flocculant dosage: ; in: : Effective volume of the reaction tank; : Turbidity reduction coefficient per unit drug dose; : On-site mixing efficiency factor; Total amount of flocculant to be added; Calculate the required dosage of oxidant: ; in: Currently detected dissolved organic matter value; Dissolved organic matter The median of the stable observation window; The flow rate diverted to the isolation tank is equal to ; Oxidizing agent stoichiometric coefficient; Required dosage of oxidant; Calculate the membrane backwash trigger criterion: ; ; Define the trigger condition for backwashing as follows: like and If so, a backwash command is issued to perform a backwashing operation on the membrane; in: Short time growth rate; Changes in membrane pressure difference between inlet and outlet sections; : Membrane pressure differential sampling interval; : Preset The growth rate threshold can be taken as the system's historical normal operating conditions. The 90th-95th percentile of the growth rate; Particle size proxy; Scattering-turbidity ratio coefficient; The current turbidity value collected; : Preset particle size proxy threshold, to With history The 90th percentile was determined, and the value was taken as follows. By calculating the dosage and treatment intensity based on site conditions, the treatment behavior is correlated with the current water quality status. This method avoids overtreatment when the abnormality is small and undertreatment when the abnormality is large. In application scenarios where leachate water quality changes drastically and irregularly, the treatment intensity can be adjusted according to the degree of abnormality, thereby improving resource utilization efficiency and maintaining stable system operation. The monitoring and handling of responses within a short response window, and updating the baseline or calibration or triggering recalibration according to rules to maintain parameter synchronization with the baseline, includes: After performing the exception handling operation, within a preset short time window Conduct a short-term response assessment to determine whether the medication has achieved its intended effect: ; like If the result is valid, then the initial short pulse recalibration is performed to update the settings. ; in: : Turbidity measured before drug administration; After administering the medication, at the window Average turbidity measured internally; : Handling changes in turbidity; Tolerance for on-site utilization of pharmaceuticals Initial deployment recommendations This means that at least 80% of the theoretical effect is expected, and adjustments can be made based on historical response data after running for a period of time. The preset short time window Can be taken as ; The updated baseline specifically refers to setting the update interval to be: ,Pick Every During the steady-state observation window The median and MAD of each sensor are resampled and calculated. By conducting short-term observations of the handling response after anomaly handling and adjusting the system's perception accordingly, the system can continuously reflect the true operating status. In environments where the composition of leachate water changes significantly in stages, this method avoids the long-term use of outdated operating perceptions, ensuring the system maintains a consistent understanding of the current operating conditions. This reduces subsequent anomaly judgment bias and enhances the system's adaptability to complex operating stages.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0025] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for the full-volume treatment of landfill leachate, characterized in that, include: Before the system is put into operation, raw time-series data within the steady-state window are collected, and baseline statistics for each sensor are established. Under steady-state conditions, a known pulse is applied and the response is recorded to obtain and store the field calibration coefficients. Real-time reading of sensor measurements and calculation of robust deviation index based on baseline statistics; The deviation indicators of each sensor are summarized according to the maximum value rule to form a composite anomaly indicator, which is then compared with a preset threshold to determine the occurrence of an anomaly. Anomaly types are identified based on the deviation ratio of representative sensors and threshold rules; Based on the identified anomaly type, determine the processing mode and traffic diversion strategy, and calculate the diversion ratio; Based on the split ratio and the on-site calibration coefficient, the required dosage of reagent and membrane protection action parameters are calculated using algebraic relationships, and execution instructions are issued. Monitor and respond within the short-term response window, and update the baseline or calibration or trigger recalibration according to the rules to keep the parameters synchronized with the baseline.

2. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The process of collecting raw time-series data within a steady-state window before the system is put into operation and establishing baseline statistics for each sensor includes: Set the steady-state observation window to 60 to 120 minutes, and record the measured values ​​of each sensor within this window at a preset sampling frequency; For each sensor, calculate and record its median value within this window; For each sensor, calculate and record its absolute median difference within this window; The median and the absolute median difference are used as baseline statistics for subsequent anomaly detection and calibration and stored in the controller.

3. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The process of applying a known pulse under steady-state conditions and recording the response to obtain and store field calibration coefficients includes: Under steady-state conditions, administer a known dose of pulsatile drug and record the average turbidity within the first ten minutes before administration and the average steady-state turbidity within the ten to thirty minute window after administration. If the turbidity does not decrease after administration, adjust the dosage and repeat until a decrease in turbidity is observed. The turbidity reduction coefficient per unit dose of flocculant is determined by the ratio of the change in turbidity before and after drug administration to the known dosage. Simultaneously, the changes in the light scattering ratio before and after the pulse were recorded, and a linear calibration coefficient between scattering and turbidity was established accordingly. Under safe and controlled conditions, a preset dose of oxidant is applied, and the average value of the dissolved organic matter indication before and after the oxidant is applied is recorded. The oxidant metering coefficient is established based on the normalized relationship between the application response and the application flow rate. The actual dissolution time of the agent in the reaction tank after dosing was measured and compared with the estimated ideal dissolution time. The mixing efficiency factor was determined by the ratio of the two and recorded.

4. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The real-time reading of sensor measurements and the calculation of robust deviation indices based on baseline statistics include: For each sensor, its current measured value is converted with the baseline median and the absolute median difference of the baseline. A conversion factor is used to convert the absolute median difference into a scale comparable to the standard deviation. The robustness deviation index of the sensor is then calculated and used for subsequent multi-parameter anomaly determination.

5. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The step of summarizing the deviation indices of each sensor according to the maximum value rule to form a composite anomaly index, and comparing it with a preset threshold to determine the occurrence of an anomaly, includes: The absolute value of the robust deviation index of each sensor in the preset sensor set used for anomaly detection is taken, and the maximum value rule is used to summarize it into a multidimensional anomaly index. The threshold for multidimensional anomaly detection is set to a scale range of four to six. When the multidimensional anomaly index reaches or exceeds the preset threshold, a transient anomaly is determined to exist.

6. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The method for identifying anomaly types based on the deviation ratio and threshold rules of representative sensors includes: When an anomaly is detected, robust deviation indices for dissolved organic matter, turbidity, and redox potential are selected. The ratio of dissolved organic matter deviation index to turbidity deviation index was used as a chemical index; a particle size proxy was constructed based on the linear calibration relationship between light scattering and turbidity. When the chemical index is not less than the preset ratio and the redox potential deviation reaches the preset negative threshold, the abnormality is judged to be chemical type. When the chemical properties index is less than the preset ratio and the particle size distribution exceeds the absolute median difference of its baseline median plus one and a half times, the abnormality is determined to be particulate.

7. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The process of determining the processing mode and traffic splitting strategy based on the identified anomaly type, and calculating the traffic splitting ratio, includes: When a chemical anomaly is detected, the diverted water is transported to an isolation tank via a bypass, and an oxidant is introduced into the isolation line for dilution and oxidation. When a particulate anomaly is identified, flocculant is added to the main line to enhance flocculation, and the membrane load ratio in the membrane treatment section is temporarily reduced. Based on the magnitude of the chemical properties, the volume fraction of the flow diverted to the isolation tank is determined using a segmented rule: Full diversion is implemented when chemical indicators reach or exceed strict thresholds; Partial diversion is implemented when the chemical properties are between two thresholds; When the chemical index is below the threshold, diversion to the isolation tank will not be implemented.

8. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The process involves calculating the required reagent dosage and membrane protection action parameters based on the split ratio and on-site calibration coefficient using algebraic relationships, and issuing execution instructions, including: After determining the integral rate of the fluid, the target turbidity of the main line is set as the absolute median difference of the median turbidity within the steady-state observation window plus a multiple of 1.

5. Based on the actual volumetric flow rate and current turbidity of the main line after diversion, combined with the flocculant efficiency coefficient and mixing efficiency factor obtained from on-site calibration, the required total flocculant dosage is determined by algebraic calculation and a dosing instruction is issued. Based on the flow rate diverted to the isolation line and the difference between the current dissolved organic matter value and its baseline median, the required oxidant dosage is determined through algebraic calculation using the oxidant metering coefficient, and a dosing instruction is issued. A combined threshold criterion of short-term membrane pressure difference increase and particle size proxy is used to trigger membrane backwashing operation.

9. The method for full-volume treatment of landfill leachate according to claim 1, characterized in that, The monitoring and handling of responses within a short response window, and updating the baseline or calibration or triggering recalibration according to rules to maintain parameter synchronization with the baseline, includes: After the treatment is performed, the turbidity changes before and after the treatment are compared with the expected changes calculated based on the dosage and calibration coefficient within a preset short time window to determine whether the treatment has achieved the predetermined response. When the actual response does not meet the expected value, a controlled short pulse recalibration is performed to update the field calibration coefficients; According to the preset update interval, the steady-state observation window is periodically resampled, and the median and absolute median difference of each sensor are recalculated to update the baseline statistics.

Images